class_docstr: Augmenter that leverage two translation models for augmentation. For example, the source is English. This
  augmenter translate source to German and translating it back to English. For detail, you may visit
  https://towardsdatascience.com/data-augmentation-in-nlp-2801a34dfc28
  
  :param str from_model_name: Any model from https://huggingface.co/models?filter=translation&search=Helsinki-NLP. As
      long as from_model_name is pair with to_model_name. For example, from_model_name is English to Japanese,
      then to_model_name should be Japanese to English.
  :param str to_model_name: Any model from https://huggingface.co/models?filter=translation&search=Helsinki-NLP.
  :param str device: Default value is CPU. If value is CPU, it uses CPU for processing. If value is CUDA, it uses GPU
      for processing. Possible values include 'cuda' and 'cpu'. (May able to use other options)
  :param bool force_reload: Force reload the contextual word embeddings model to memory when initialize the class.
      Default value is False and suggesting to keep it as False if performance is the consideration.
  :param int batch_size: Batch size.
  :param int max_length: The max length of output text.
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.word as naw
  >>> aug = naw.BackTranslationAug()
code: def get_model(
      cls,
      from_model_name,
      to_model_name,
      device="cuda",
      force_reload=False,
      batch_size=32,
      max_length=None,
  ):
      return init_back_translation_model(
          from_model_name,
          to_model_name,
          device,
          force_reload,
          batch_size,
          max_length,
      )
  
file_path: nlpaug/nlpaug/augmenter/word/back_translation.py
imports: import string
  import os
  from nlpaug.augmenter.word import WordAugmenter
  import nlpaug.model.lang_models
label_desc: Use image node_id 3 for calling the BackTranslationAug obj's underlying member method code with example usage: obj.get_model(cls, from_model_name, to_model_name, device, force_reload, batch_size, max_length) and returns: init_back_translation_model